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Creating Topic Hierarchies for Large Medical Libraries

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Knowledge Representation for Health-Care. Data, Processes and Guidelines (KR4HC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5943))

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Abstract

Web-based medical digital libraries contain a huge amount of valuable, up-to-date health care information. However, their size, their keyword-based access methods and their lack of semantic structure make it difficult to find the desired information. In this paper we present an automatic, unsupervised and domain-independent approach for structuring the resources available in an electronic repository. The system automatically detects and extracts the main topics related to a given domain, building a taxonomical structure. Our Web-based system is integrated smoothly with the digital library’s search engine, offering a tool for accessing the library’s resources by hierarchically browsing domain topics in a comprehensive and natural way. The system has been tested over the well-known PubMed medical library, obtaining better topic hierarchies than those generated by widely-used taxonomic search engines employing clustering techniques.

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Authors and Affiliations

  1. ITAKA-Intelligent Technologies for Advanced Knowledge Acquisition Department of Computer Science and Mathematics, University Rovira i Virgili, Av. Països Catalans, 26, 43007, Tarragona, Spain

    David Sánchez & Antonio Moreno

Authors
  1. David Sánchez
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  2. Antonio Moreno
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Editor information

Editors and Affiliations

  1. Rovira i Virgili University, Av. Països Catalans 26, 43007, Tarragona, Spain

    David Riaño

  2. Free University Amsterdam, De Boelelaan 1081A, 1081HV, Amsterdam, The Netherlands

    Annette ten Teije

  3. Danube University Krems, Dr.-Karl-Dorrek-Str. 30, 3500, Krems, Austria

    Silvia Miksch

  4. University of Haifa, Rabin Bldg, 31905, Haifa, Israel

    Mor Peleg

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Sánchez, D., Moreno, A. (2010). Creating Topic Hierarchies for Large Medical Libraries. In: Riaño, D., ten Teije, A., Miksch, S., Peleg, M. (eds) Knowledge Representation for Health-Care. Data, Processes and Guidelines. KR4HC 2009. Lecture Notes in Computer Science(), vol 5943. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11808-1_1

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  • DOI: https://doi.org/10.1007/978-3-642-11808-1_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11807-4

  • Online ISBN: 978-3-642-11808-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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